ROMark: A Robust Watermarking System Using Adversarial Training
arXiv:1910.01221
Abstract
The availability and easy access to digital communication increase the risk of copyrighted material piracy. In order to detect illegal use or distribution of data, digital watermarking has been proposed as a suitable tool. It protects the copyright of digital content by embedding imperceptible information into the data in the presence of an adversary. The goal of the adversary is to remove the copyrighted content of the data. Therefore, an efficient watermarking framework must be robust to multiple image-processing operations known as attacks that can alter embedded copyright information. Another line of research \textit{adversarial machine learning} also tackles with similar problems to guarantee robustness to imperceptible perturbations of the input. In this work, we propose to apply robust optimization from adversarial machine learning to improve the robustness of a CNN-based watermarking framework. Our experimental results on the COCO dataset show that the robustness of a watermarking framework can be improved by utilizing robust optimization in training.
5 pages, 1 figure, Machine Learning with Guarantees workshop at NeurIPS 2019
References in corpus (2)
Cited by in corpus (9)
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- Generating Image Adversarial Examples by Embedding Digital Watermarks
- Distortion Agnostic Deep Watermarking
- Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings
- From Attack to Protection: Leveraging Watermarking Attack Network for Advanced Add-on Watermarking
- Towards transformation-resilient provenance detection of digital media
- Robust watermarking with double detector-discriminator approach